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<h1><a href="https://nn.labml.ai/diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>
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<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper <a href="https://papers.labml.ai/paper/2006.11239">Denoising Diffusion Probabilistic Models</a>.</p>
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<p>In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images.</p>
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<p>Here is the <a href="https://nn.labml.ai/diffusion/ddpm/unet.html">UNet model</a> that predicts the noise and <a href="https://nn.labml.ai/diffusion/ddpm/experiment.html">training code</a>. <a href="https://nn.labml.ai/diffusion/ddpm/evaluate.html">This file</a> can generate samples and interpolations from a trained model. </p>
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